Senior Data Scientist, Fraud Risk Detection
Experian is hiring a Senior Data Scientist, Fraud Risk Detection for a remote role open to applicants in United States. Level rates it ; you can apply on Experian.
Level's read on this job
- On Level's scale, this data scientist role is Builds AI ●●●●: the job is building AI systems.
- The posting lists this role as remote.
- Tools and skills the posting cites include scikit-learn, Databricks and AI evaluation.
- Level lists 369 other open roles at Experian.
- The role was posted in the last few days.
AI in this role
Experian is a global data and technology company, powering opportunities for people and businesses around the world. We operate across a range of markets, from financial services to healthcare, automotive, agribusiness, insurance, and many more. Experian invests in people and new advanced technologies to unlock the power of data. We have an amazing team of 25,200 people in 32 countries.
Overview
Experian's Fraud Analytics & Commercialization operates across four main functions. These include client engagement analytics, scalable and custom analytics for financial institutions, fraud analytics consulting, and solution integrity and enablement for production-ready platforms.
We're looking for a motivated Data Scientist to help build fraud detection models and features that identify high-risk activity while minimizing friction for legitimate customers. Core skills for this role include strategic thinking, an eagerness to collaborate, and empathy. You will will dig into surprising signals in the data and to learn how that insight becomes a deployed model.
You will help investigate the latest fraud patterns, build features, and train and evaluate machine learning models. You will work with senior data scientists and engineers starting with problem definition through feature engineering, experimentation, and deployment. You will be a developing programmer, ready to translate theoretical principles into production-ready solutions.
We continue to sharpen through research and the engineering that turns those findings into tools and systems built for commercialization.
This is a remote role and you will report into the Sr. Manager of Fraud Analytics.
What you'll do
- Explore complex datasets, with guidance from senior team members, to identify fraud patterns, attack methods, and behavioral signals.
- Work with senior data scientists to translate fraud questions into testable hypotheses
- Help build machine learning models for fraud detection across account opening, account takeover, and identity risk.
- Evaluate models using both technical and business metrics, such as precision, recall, fraud capture rate, false-positive rate, and customer friction.
- Develop and validate features using identity, transactional, behavioral, and other available data sources.
- Write clean, well-tested code, and work with engineering to bring models and features into production.
- Partner with the score monitoring team to help set up model and feature monitoring, and support research on related client questions.
- Help prepare analyses and communicate findings to both technical and nontechnical audiences.
- Apply Experian's standards for data privacy, model documentation, explainability, validation, and governance.
- 3+ years of experience in data science, machine learning, statistical modeling, or a related quantitative field
- Bachelor's or advanced degree in computer science, statistics, mathematics, economics, engineering, data science, or another quantitative discipline
- Foundation in supervised learning, model evaluation, feature selection, statistical inference, and techniques such as classification and anomaly detection.
- Proficiency in Python, with the ability to write clean, readable, and well-tested code.
- Familiarity with common data science and machine-learning tools such as pandas, NumPy, and scikit-learn.
- Investigative mindset and the ability to move from unusual data patterns to testable hypotheses.
- Familiarity with PySpark, cloud platforms such as Amazon Web Services, Google Cloud, Azure, Databricks, and Snowflake, or other large-scale data tools.
- Exposure to financial services, FinTech, payments, or another regulated or fraud-intensive industry, through coursework, internship, or prior work.
- #LI-Remote
Benefits/Perks:
- Great compensation package and bonus plan
- Core benefits including medical, dental, vision, and matching 401K
- Flexible work environment, ability to work remote, hybrid or in-office
- Flexible time off including volunteer time off, vacation, sick and 12-paid holidays
- Explore all our exciting benefits here: https://yourexperianbenefits.com/cand-index.html
Our uniqueness is that we celebrate yours. Experian's people first, inclusive and purpose-driven culture is multi award-winning. We have won World's Best Workplaces™ 2025 (Fortune Global Top 25), and Great Place To Work™ in 26 countries to name a few.
Experian's recruitment process is conducted only through authorised channels. Recruitment communications will only be sent from an @experian.com email address. Experian will never ask candidates to make any payment as part of an application, interview, assessment, onboarding, or recruitment process.
Our compensation reflects the cost of labor across several U.S. geographic markets. Within this range, individual pay is determined by work location and additional factors such as job-related skills, experience, and education. You will be eligible for a variable pay opportunity and a comprehensive benefits package.
Experian is proud to be an Equal Opportunity Employer for all groups protected under applicable federal, state and local law, including protected veterans and individuals with disabilities. If you have a disability or special need that requires accommodation, please let us know at the earliest opportunity.
How we rate this
Senior Data Scientist, Fraud Risk Detection at Experian rates 86 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Also common in data scientist jobs at this level
Pay
- Typical data scientist job
- $165k
This posting doesn't list pay. Use the typical data scientist figure when pay comes up. See data scientist jobs
Questions you could be asked
- How do you decide that one model's output is better than another's for a given task?
- Walk me through how you've used scikit-learn in your day-to-day work.
- What are the limits of Databricks that you've run into, and how did you work around them?
- How would you decide a model or AI system is ready to ship?
- Tell me about a time a model underperformed in production. How did you find out, and what did you change?
Adapt your resume
- List these exact terms on your resume: AI Evaluation, scikit-learn, and Databricks. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
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